Data is like gold dust for ambitious start-ups and scale-ups.

Equipped with the right data, growing businesses can achieve everything from skyrocketing ROI to launching new products and securing vital fundraising.

It can also deliver extremely valuable insights into where spending can be reduced or focused.

However, if companies want to harness the full power of their data, they need to hire experts who can collect, analyze, and leverage it effectively. In other words, they need to assemble a skilled data team.

But for many start-ups and scale-ups, this is easier said than done.

While many founders are aware of the importance of data, they’re not entirely sure what kind of talent they need to utilize it. Do they need architects, engineers, analysts, scientists or a combination? And where should they start with building a data team from scratch?

Data experts can be transformative for scaling businesses, so it’s crucial for founders to figure out how they can build and expand their own data team. In highly competitive industries, the right data strategies can give start-ups/scale-ups a vital edge - and an opportunity to drive long-term growth.

In this post, we’ll be explaining how growing businesses can assemble a market-leading data team - from making their very first hire to building out a versatile, multi-talented department.

Where to start with building a data team?

Getting started with building a data team can be daunting for founders.

‘Data’ is an extremely broad term, and so it’s difficult to know which types of data might be required - and who will be best placed to offer support.

A lot will revolve around the infrastructure of a business. For example, are there multiple departments that will require data support, or is this a small company with a centralized team? Is data currently being used (but processes need refining) or is a scale-up starting from scratch?

There’s no one-size-fits-all approach to building a data team, but there are some practical questions that can help founders narrow their search. For example:

• What does the business do?

• What does the business need data for?

• Which teams/departments need data support the most?

• How will data primarily be leveraged in the short term?

• Are there clear long-term plans/ambitions for data usage?

• Does the data team need to be centralized or decentralized?

• What KPI’s need to be tracked as non-negotiable from the off?

These are fundamental questions that founders should ask themselves, and by establishing the answers, they’ll start to paint a picture of the data support that’s required.

(However, it’s also important to remember that some data is better than no data. The role of data in a business will inevitably evolve, so it’s better to stay agile and adapt a data team rather than never building one at all.)

Making the all-important first data hire

Once a start-up or scale-up has broadly figured out the type of data expertise they need, they can start to think about their first hire.

A golden rule here is that the first data hire should generally be an experienced ‘jack of all trades’, not a hyper-focused specialist.

A start-up/scale-up will find that their data needs to change over time, and initially, they’ll just need an expert to get the ball rolling. More specialist hires can be brought in later down the line, but in these early stages, a growing business needs a versatile data hire who can:

1. Engineer (i.e. find) the data that’s required

2. Analyze this data to identify the most valuable elements

3. Explain the value of the data clearly to stakeholders

4. Explore different ways to leverage this data

The first data hire isn’t about perfecting a complex data strategy. It’s about laying down the foundations for data collection, analysis, and usage - bringing in a generalist who understands the role and can start adding tangible value to the business through data.

Using the term generalist may seem blasé and not an accurate job title to hire. By this term I’m referring to someone who has both an engineer and analyst skillset (job descriptions below). Ideally someone who can obtain the ‘raw’ data and also analyze it, in short, make it make sense.

It’s also essential that founders find someone who is both technically strong and can clearly communicate their strategy in plain language, getting stakeholders and departments on board.

(Complex technical jargon won’t be useful in these early stages, so it’s key to balance data knowledge with communication/interpersonal skills when making the first hire!)

Ultimately, this generalist hire will help companies get to grips with their data and start driving increased revenue. They’ll enable founders to make data-backed decisions with confidence and lay down the building blocks for future expansion.

Building out an effective data team

The first data hire will (ideally) have an immediate impact on a start-up or scale-up.

As a generalist, they’ll be able to support multiple departments (e.g. marketing, sales, finance) in collecting valuable data, driving significant efficiencies, and identifying opportunities. Growing businesses should see a clear value in data usage, and this will be reflected in their revenue.

But where should founders look next once their first hire is delivering results?

Well, it depends on the direction of the business. If a data hire is struggling to keep up with demand across multiple teams, it’s a good idea to bring in additional generalists who can specialize in specific departments and share the workload.

However, start-ups/scale-ups will eventually need to look at specialist data hires who can tackle more complex tasks and open new doors. There are plenty of different job titles to consider, but here are some of the most common ones:

Data Analyst: A skilled data analyst will be able to set up intuitive dashboards (e.g. Tableau, Power BI) that help visualize complex data. They’ll help businesses extract more insights from raw data and improve decision-making.

Data Engineer: A top Engineer will optimize the data infrastructure for data collection, management and transformation. They will create strong pipelines that will be able to convert the raw data into usable formats for Analysts and Scientists to work with.

Data Architect: A data architect will establish and design a solid data infrastructure - e.g. ETL pipelines and data warehouses - that makes transferring and storing data much easier. This is particularly important once start-ups begin using data at scale, as architects will help to streamline processes, reduce storage costs, and improve data quality. Having a solid infrastructure also fulfills any regulatory or industry-specific requirements by reducing audit times and complexities.

Data Manager: A data manager will essentially coordinate every individual and process within a data team. They’ll ensure that everything is running as smoothly and efficiently as possible, which is critical when data needs become more complex.

Data Scientist: A strong data scientist can be an extremely valuable hire. These experts are very challenging to recruit, but offer an in-depth knowledge of AI/machine learning that can lead to incredibly powerful data solutions and efficiency-driving systems.

The number of specialists available may seem overwhelming, but a strong data leader can help founders understand which experts are required and when they’ll make the biggest impact.

What are some best practices for assembling a successful data team?

While building a new data team is challenging, there are a few best practices that can help founders recruit with confidence.

Have a clear plan

Make sure you have a reason why and how you want to utilize data in your business. What are some of your key objectives and things you need insight on? Building out data is an investment to the business but not a cheap one, you need implement it with purpose and direction.

Internal auditing

Before diving in with your first hire, I’d advise auditing your current talents and seeing who/what can be utilized. What data can you already access? How can you utilize that? Are you using that?

To set yourself up for success I would recommend taking a step back and doing an internal evaluation of the existing talent in the business set against your strategic goals for the next 6-18 months. From there you’ll be able to identify where the skill gaps lie and you can start to shape precisely where the business really needs to make some new hires or redefine the roles and responsibilities of the existing team.

Hire someone versatile

Early-stage start-ups don’t always have the luxury of being able to make multiple hires, it’s important those that you bring in are human Swiss Army knives that can wear multiple hats and have impact across the business.

Ensuring you have a diverse team will encourage versatility, innovation and outside-the-box thinking that will look at problems from a different perspective. If every member of the data team fits a similar profile, it’s unlikely to lead to creative ideas and unique solutions. Diversity is a recipe for innovation.

Focus on clear, consistent communication

Data teams are often incredibly fast-moving and dynamic, especially in rapidly growing start-ups/scale-ups. As a result, clear communication and consistent collaboration are vital for success.

It’s far too easy for conversations around data to descend into technical jargon, which isn’t helpful for key stakeholders who aren’t experts. That’s why communication is so important - if a new data hire can lead conversations and explain strategies in simple language, it becomes much easier for different teams to buy in and start benefiting.

Speak to an expert

Navigating your first data hire can be a daunting task but making the right hire can be a game-changer for startups.

If you're unsure which data role is most crucial for your business, it's wise to consult a data expert. They can help you clearly define the responsibilities needed, identify the right level of expertise, provide salary benchmarks, and guide you in finding the ideal candidate who aligns with your specific needs and goals.

If you’re considering building out your BI / Data function please reach out to Chris at [email protected]